However, audio-visual representation learning is still challenging for image manipulation because there is as much positive pairs as CLIP [1]} to learn the correlation between different modalities.
CLIP learned the relationship between image and text embedding through the multi-modal self-supervised learning of 400 million image-text pairs and show a zero-shot inference performance comparable to supervised learning in most image-text benchmark datasets.
